Rabbit Anti-Human Cadherin-19 (a.a. 371-420) Polyclonal antibody

Introduction: Gestational (GC) (derived from the placenta) and non-gestational (NGC) choriocarcinomas are trophoblastic diseases originated from abnormal proliferation of trophoblastic cells. These rare tumors share similar morphology and pathological features and differ on chemotherapy response, genetic origin and prognosis. In this study, the genomic profile of choriocarcinomas was performed according to their origin (GC or NGC) aiming to better understand these poorly characterized diseases. Methods: Thirteen patients were included in this study; 10 presented previous history of hydatidiform mole and six developed metastasis. Twelve polymorphic microsatellite markers (D15S659, APOC2, D5S816, BAT25, D3S1614, D3S1311, D1S1656, APC-D5S346, D3S1601, 018570, D8S1110 and D11S1999) were investigated to distinguish GC from NGC. All choriocarcinomas were evaluated by copy number alterations using array CGH. Results: Eight cases were classified as GC and five as NGC. Although potentially polymorphic, NGC exhibited significant gain of 21p11. Rare copy number alterations (CNA) were detected as a frequent event in GC including gains of 1p36.33-p36.32 (3 cases), 17q25.3 (4 cases), and losses of 9q33.1 (5 cases), 17q21.3 (3 cases) and 18q22.1 (4 cases) (varying from 724 to 3,053 Kb). Discussion: Two tumor suppressor genes are candidates to be involved in GC: TRIM32 (9q33.1) and CDH19 (18q22.1). Gains of CBX2, CBX4 and CBX8 were frequently found in high risk prognostic score in GC. The in silico functional interaction analysis revealed the involvement of PTEN and PI3K-Akt signaling pathways. These data pointed out significant genomic alterations in GC, opening new avenues to better characterize the pathobiology of this disease. (C) 2016 Elsevier Ltd. All rights reserved.

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) <= 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.